Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network

Ali, Jaouher Ben; Hamdi, Takoua; Fnaiech, Nader; Costanzo, V. Di; Fnaiech, Farhat; Ginoux, Jean-Marc

doi:10.1016/j.bbe.2018.06.005

Cited by 96 publications

(30 citation statements)

References 35 publications

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“…When given no testing sample independent of the training sample, one can randomly select and hold out a portion of the training sample for testing, and construct a prediction with only the remaining sample. Typically, 30% of the training sample is set aside for testing and 70% is used for the training step [ 58 - 60 ].…”

Section: Methodsmentioning

confidence: 99%

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

et al. 2020

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Background Uric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population. Objective The aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs. Methods Various machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh. Results The mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range <7.0 mg/dL). Therefore, these individuals should be monitoring their uric acid regularly. The boosted decision tree regression model showed the best performance among the models tested based on the root mean squared error of 0.03, which is also better than that of any previously reported model. Conclusions A uric acid prediction model was developed based on personal characteristics, dietary information, and some basic health checkup measurements. This model will be useful for improving awareness among high-risk individuals and populations, which can help to save medical costs. A future study could include additional features (eg, work stress, daily physical activity, alcohol intake, eating red meat) in improving prediction.

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Section: Methodsmentioning

confidence: 99%

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

et al. 2020

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“…1 In this work, Long-Short term memory (LSTM) neural network is trained to predict the BG levels of a patient given their prior blood sugars. Similar work has been done in [1,2,3,4,5]. I refer the reader to [1,3] for descriptions of the LSTM method.…”

Section: Introductionmentioning

confidence: 90%

Setting Limits on Neural Network’s Predictive Capacity in T1D Blood Glucose Concentration

Chuna

2020

Preprint

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The ability to anticipate changes in blood glucose (BG) concentration would have a great impact on Type 1 diabetics (T1D). In order to create T1D treatment plans, patients collect a BG concentration time series. It has been demonstrated that various types of recurrent neural networks, such as Long Short Term Memory (LSTM), have success forecasting T1D BG concentrations. However, limited work has been done to characterize the T1D time series or set limits on neural network's predictive capacity. In this work, a T1D patient's 14 day BG concentration time series is studied. First, I test the time series stationarity. Then I use auto-correlation analysis, spectral analysis, and Gaussian process regression to characterize the T1D BG time series. Finally, the LSTM's prediction quality is quantified and interpreted at different prediction intervals. The success or failure of the LSTM's predictions are interpreted using the characterization of the time series.

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“…Ali et al 5 have proposed a method based on artificial neural networks (ANN) for blood glucose level prediction of T1D patients. The authors have used a dataset derived from 12 T1D patients.…”

Section: Related Workmentioning

confidence: 99%

“…There are many studies conducted in the literature on the prediction of blood glucose levels by using different techniques. 3,[5][6][7][8][9][10] Since there is no specific rule of machine learning techniques and parameter optimization, the trial and error approaches are often used. Therefore, experiments with different machine learning methods enrich and improve the recent literature.…”

Section: Introductionmentioning

confidence: 99%

Blood glucose level prediction for diabetes based on modified fuzzy time series and particle swarm optimization

Ozogur

Orman

2020

Computational Intelligence

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Blood glucose control is an essential goal for the patients who have Type-1 diabetes (T1D). The prediction of the blood glucose levels for the next 30-minute is crucial. If the predicted blood glucose level is in the critical ranges, and these predictions can be known in advance, then the patients can take the necessary cautions to prevent from it. In this article, we propose a modified fuzzy particle swarm optimization algorithm for the prediction of blood glucose levels of 30-minute after the last measurement. We form the average and patient-specific models to predict the blood glucose level of the patients. Both models are tested on two different datasets which contain patients with T1D. The experimental results are evaluated in terms of root mean squared error and Clarke error grid analysis metrics. The results indicate that our proposed modified algorithm is feasible to be applied to the prediction of blood glucose levels. In addition, this approach can assist patients with T1D for their blood glucose control.

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Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network

Cited by 96 publications

References 35 publications

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

Setting Limits on Neural Network’s Predictive Capacity in T1D Blood Glucose Concentration

Blood glucose level prediction for diabetes based on modified fuzzy time series and particle swarm optimization

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